| dc.contributor.author | Xu, Xing John | |
| dc.description.abstract | Farmers always have been concerned about the quantity of crops (yield) as well as
the quality of crops (sugar content of the sugar beets). The quality and quantity of crops
are affected by various attributes, some are natural elements (rain, sunshine etc) and some
are not (the amount of fertilizer, seed type etc). Some techniques have been developed to
discover attributes that are important to different crops’ yield. But within those selected
attributes, how can we tell one attribute is more important than the other? The proposed
algorithm is aimed to utilize the advantages of multiple response attributes to select the
important attributes and then put the selected attributes in a hierarchical order. Although
at the end this paper only focuses on yield prediction, any other target attribute can be a
candidate for the prediction model. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Multi-Variate Attribute Selection for Agricultural Data | en_US |
| dc.type | Thesis | en_US |
| dc.date.accessioned | 2018-02-26T18:21:52Z | |
| dc.date.available | 2018-02-26T18:21:52Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | https://hdl.handle.net/10365/27612 | |
| dc.identifier.orcid | 0000-0002-4620-416X | |
| dc.description.sponsorship | Grant No. 1114363 from National Science Foundation | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Denton, Anne M. | |
